This project will develop neuroimaging methods for acquisition and mapping of brain metabolites by short-TE proton MR Spectroscopic Imaging (MRSI). This measurement is typically complicated by spectral overlap, lineshape distortions, lipid contamination, and subject motion, and these effects limit the accuracy of the metabolite measurement as well as the number of metabolites that can potentially be detected. These limitations will be addressed by using high-speed acquisition methods to obtain multiple volumetric MRSI data sets that are each encoded with varying spectral information. Data analysis methods will be developed using multidimensional parametric modeling analysis approaches, to provide improved discrimination of overlapping spectral components. In addition, higher spatial resolution acquisition, field inhomogeneity correction, and motion compensation methods will be simultaneously included to obtain data with improved spatial response and spectral quality. Acquisition sequences will be implemented that exploit both spin relaxation and evolution parameters, and evaluated in normal subjects. Parameters will be optimized using numerical simulation techniques to maximize spectrat discrimination and signal to noise ratios, while also limiting the number of independent spectral measurements required. These same numerical methods will also be used to provide the prior information required for the spectral analysis procedures. The developed methods will be applied to mapping all metabolites observed using short-TE proton MR spectroscopy in normal human brain, and a 3D image database of metabolite levels will be generated. This will make use of spatial transformation and normalization procedures obtained through IRPG interactions with researchers at the MRS Unit, San Francisco (PIG. Matson). This development will establish the range and variance of normal metabolite profiles and provide valuable comparative data for numerous clinical investigations that use short-TE 1H MRS.